Blue blood, red blood. How does the color of an emotional scene affect visual attention and pupil size?

Pilarczyk, Joanna; Kuniecki, Michał; Sterna, Radosław

doi:10.1016/j.visres.2020.04.008

Cited by 7 publications

(4 citation statements)

References 48 publications

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“…Research shows that when subjects watch pictures, the factors that affect emotions are not only color but also semantic content. Pilarczyk et al 33 discussed the processing function of color in emotional scenes, the content of display through visual experiments, and the influence of image color and content on pupil and visual attention. Frey et al 34 believed that the guiding role of color depends largely on the content category of the image, so the analysis of different categories may produce different results.…”

Section: Literature Reviewmentioning

confidence: 99%

Experimental study on the relationship between the harmony and cognitive load of business intelligence dashboard color combinations

Tan

Liu

2021

Color Research & Application

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The color combination is an important factor affecting dashboard visual design and is key to triggering the operator's visual harmony and emotion. However, in the actual design process, the relationship among the different color schemes of a dashboard and the operator's harmony, pleasure, and cognitive load has not been effectively analyzed. To solve this problem, two methods, questionnaire measurement and eye movement tracking, were used to evaluate the effects of 24‐color combinations under four‐color schemes of a dashboard by analyzing the changes in color harmony, pleasure, eye movement indicators. The four schemes were red–yellow–blue, green–purple–orange, blue green–blue purple–yellow green, and red orange–yellow orange–red purple. The research results show that the degrees of color harmony and pleasure show a positive correlation. Cognitive load is not affected by color harmony. The larger the amount of information, the higher the cognitive load. This research can be used as a reference when designing and optimizing the color scheme of a business intelligence dashboard.

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Section: Literature Reviewmentioning

confidence: 99%

Experimental study on the relationship between the harmony and cognitive load of business intelligence dashboard color combinations

Tan

Liu

2021

Color Research & Application

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“…Images of the real world and of the objects within it reveal the spatial and spectral complexity of natural surfaces 1 and their illumination 2 , 3 . But the impression of chromatic detail is likely to be founded on partial information, limited by our peripheral color awareness 4 , pattern of gaze 5 , attention 6 , memory 7 , and the emotional and semantic content of the scene 8 , 9 , and, more fundamentally, by the relative abundances or frequencies of the different colors present 10 , 11 . Of course, not all the colors in a scene are needed in order to describe or remember it, and different methods have been used to estimate which colors are relevant.…”

Section: Introductionmentioning

confidence: 99%

Efficient quantization of painting images by relevant colors

Tirandaz

Foster

Romero

et al. 2023

Sci Rep

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Realistic images often contain complex variations in color, which can make economical descriptions difficult. Yet human observers can readily reduce the number of colors in paintings to a small proportion they judge as relevant. These relevant colors provide a way to simplify images by effectively quantizing them. The aim here was to estimate the information captured by this process and to compare it with algorithmic estimates of the maximum information possible by colorimetric and general optimization methods. The images tested were of 20 conventionally representational paintings. Information was quantified by Shannon’s mutual information. It was found that the estimated mutual information in observers’ choices reached about 90% of the algorithmic maxima. For comparison, JPEG compression delivered somewhat less. Observers seem to be efficient at effectively quantizing colored images, an ability that may have applications in the real world.

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“…Owing to these features, these procedures have been used to tackle a wide range of research problems. To name a few, they allowed to characterize factors influencing oculomotor behavior while viewing emotionally laden images ( Pilarczyk, Schwertner, Wołoszyn, & Kuniecki, 2019 ; Pilarczyk, Kuniecki, Wołoszyn, & Sterna, 2020 ; Pilarczyk & Kuniecki, 2014 ) or images depicting people ( Birmingham, Bischof, & Kingstone, 2008 ; Cerf, Paxon Frady, & Koch, 2009 ; End & Gamer, 2017 ; Flechsenhar & Gamer, 2017 ), as well as to uncover idiosyncrasies in gaze behavior of individuals ( Broda & de Haas, 2022 ; De Haas, Iakovidis, Schwarzkopf, & Gegenfurtner, 2019 ; Linka & de Haas, 2020 ; Zangrossi, Cona, Celli, Zorzi, & Corbetta, 2021 ) and create robust methods of predicting fixation locations ( Kümmerer, Bethge, & Wallis, 2017 ; Kümmerer, Wallis, Gatys, & Bethge, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Influence of prior knowledge on eye movements to scenes as revealed by hidden Markov models

Pedziwiatr,

Heer,

Coutrot

et al. 2023

Journal of Vision

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Human visual experience usually provides ample opportunity to accumulate knowledge about events unfolding in the environment. In typical scene perception experiments, however, participants view images that are unrelated to each other and, therefore, they cannot accumulate knowledge relevant to the upcoming visual input. Consequently, the influence of such knowledge on how this input is processed remains underexplored. Here, we investigated this influence in the context of gaze control. We used sequences of static film frames arranged in a way that allowed us to compare eye movements to identical frames between two groups: a group that accumulated prior knowledge relevant to the situations depicted in these frames and a group that did not. We used a machine learning approach based on hidden Markov models fitted to individual scanpaths to demonstrate that the gaze patterns from the two groups differed systematically and, thereby, showed that recently accumulated prior knowledge contributes to gaze control. Next, we leveraged the interpretability of hidden Markov models to characterize these differences. Additionally, we report two unexpected and interesting caveats of our approach. Overall, our results highlight the importance of recently acquired prior knowledge for oculomotor control and the potential of hidden Markov models as a tool for investigating it.

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Blue blood, red blood. How does the color of an emotional scene affect visual attention and pupil size?

Cited by 7 publications

References 48 publications

Experimental study on the relationship between the harmony and cognitive load of business intelligence dashboard color combinations

Experimental study on the relationship between the harmony and cognitive load of business intelligence dashboard color combinations

Efficient quantization of painting images by relevant colors

Influence of prior knowledge on eye movements to scenes as revealed by hidden Markov models

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